2017/02/08 by Alexis Pibrac, Pibrac, Alexis, Bilal Farooq +1
Engineering · Mathematics · Physics and Astronomy · Social Sciences · #Evacuation and Crowd Dynamics #FOS: Mathematics #FOS: Physical sciences #Optimization and Control (math.OC) #Physics and Society (physics.soc-ph) #Traffic control and management #Transportation Planning and Optimization #math.OC #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1702.02624
arxiv created 2017/02/08 · openalex publication_date 2017/02/08 · arxiv updated 2017/02/10 · openalex created_date 2019/05/29 · openalex updated_date 2026/07/28
We present an integrated microsimulation framework to estimate the pedestrian movement over time and space with limited data on directional counts. Using the activity-based approach, simulation can compute the overall demand and trajectory of each agent, which are in accordance with the available partial observations and are in response to the initial and evolving supply conditions and schedules. This simulation contains a chain of processes including: activities generation, decision point choices, and assignment. They are considered in an iteratively updating loop so that the simulation can dynamically correct its estimates of demand. A Markov chain is constructed for this loop. These considerations transform the problem into a convergence problem. A Metropolitan Hasting algorithm is then adapted to identify the optimal solution. This framework can be used to fill the lack of data or to model the reactions of demand to exogenous changes in the scenario. Finally, we present a case study on Montreal Central Station, on which we tested the developed framework and calibrated the models. We then applied it to a possible future scenario for the same station.